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Rethinking Probabilistic Topic Modeling from the Point of View of Classical Non-Bayesian Regularization

In: Data Analysis and Optimization

Author

Listed:
  • Konstantin Vorontsov

    (Federal Research Center “Computer Science and Control” of RAS and Institute of Artificial Intelligence of M.V.Lomonosov Moscow State University)

Abstract

Probabilistic Topic Modeling with hundreds of its models and applications has been an efficient text analysis technique for almost 20 years. This research area has evolved mostly within the frame of the Bayesian learning theory. For a long time, the possibility of learning topic models with a simpler conventional (non-Bayesian) regularization remained underestimated and rarely used. The framework of Additive Regularization for Topic Modeling (ARTM) fills this gap. It dramatically simplifies the model inference and opens up new possibilities for combining topic models by just adding their regularizers. This makes the ARTM a tool for synthesizing models with desired properties and gives rise to developing the fast online algorithms in the BigARTM open-source environment equipped with a modular extensible library of regularizers. In this paper, a general iterative process is proposed that maximizes a smooth function on unit simplices. This process can be used as inference mechanism for a wide variety of topic models. This approach is believed to be useful not only for rethinking probabilistic topic modeling, but also for building the neural topic models increasingly popular in recent years.

Suggested Citation

  • Konstantin Vorontsov, 2023. "Rethinking Probabilistic Topic Modeling from the Point of View of Classical Non-Bayesian Regularization," Springer Optimization and Its Applications, in: Boris Goldengorin & Sergei Kuznetsov (ed.), Data Analysis and Optimization, pages 397-422, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-31654-8_24
    DOI: 10.1007/978-3-031-31654-8_24
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